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Estimation, Learning and Parameters of Interest in a Multiple Outcome Selection Model


  • Justin Tobias


We describe estimation, learning, and prediction in a treatment-response model with two outcomes. The introduction of potential outcomes in this model introduces four cross-regime correlation parameters that are not contained in the likelihood for the observed data and thus are not identified. Despite this inescapable identification problem, we build upon the results of Koop and Poirier (1997) to describe how learning takes place about the four nonidentified correlations through the imposed positive definiteness of the covariance matrix. We then derive bivariate distributions associated with commonly estimated “treatment parameters” (including the Average Treatment Effect and effect of Treatment on the Treated), and use the learning that takes place about the nonidentified correlations to calculate these densities. We illustrate our points in several generated data experiments and apply our methods to estimate the joint impact of child labor on achievement scores in language and mathematics.

Suggested Citation

  • Justin Tobias, 2006. "Estimation, Learning and Parameters of Interest in a Multiple Outcome Selection Model," Econometric Reviews, Taylor & Francis Journals, vol. 25(1), pages 1-40.
  • Handle: RePEc:taf:emetrv:v:25:y:2006:i:1:p:1-40 DOI: 10.1080/07474930500545421

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    References listed on IDEAS

    1. Angelo Melino, 1982. "Testing for Sample Selection Bias," Review of Economic Studies, Oxford University Press, vol. 49(1), pages 151-153.
    2. Nawata, Kazumitsu, 1995. "Estimation of sample-selection models by the maximum likelihood method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 39(3), pages 299-303.
    3. Olsen, Randall J, 1980. "A Least Squares Correction for Selectivity Bias," Econometrica, Econometric Society, vol. 48(7), pages 1815-1820, November.
    4. Leung, Siu Fai & Yu, Shihti, 1996. "On the choice between sample selection and two-part models," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 197-229.
    5. Kazumitsu Nawata & Michael McAleer, 2001. "Size Characteristics Of Tests For Sample Selection Bias: A Monte Carlo Comparison And Empirical Example," Econometric Reviews, Taylor & Francis Journals, vol. 20(1), pages 105-112.
    6. Chesher, Andrew & Spady, Richard, 1991. "Asymptotic Expansions of the Information Matrix Test Statistic," Econometrica, Econometric Society, vol. 59(3), pages 787-815, May.
    7. Orme, Chris, 1990. "The small-sample performance of the information-matrix test," Journal of Econometrics, Elsevier, vol. 46(3), pages 309-331, December.
    8. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
    9. Mroz, Thomas A, 1987. "The Sensitivity of an Empirical Model of Married Women's Hours of Work to Economic and Statistical Assumptions," Econometrica, Econometric Society, vol. 55(4), pages 765-799, July.
    10. Nawata, Kazumitsu, 1994. "Estimation of sample selection bias models by the maximum likelihood estimator and Heckman's two-step estimator," Economics Letters, Elsevier, vol. 45(1), pages 33-40, May.
    11. Olsen, Randall J, 1982. "Distributional Tests for Selectivity Bias and a More Robust Likelihood Estimator," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 23(1), pages 223-240, February.
    12. Greene, William H, 1981. "Sample Selection Bias as a Specification Error: Comment," Econometrica, Econometric Society, vol. 49(3), pages 795-798, May.
    13. Nawata, Kazumitsu, 1993. "A note on the estimation of models with sample-selection biases," Economics Letters, Elsevier, vol. 42(1), pages 15-24.
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    Cited by:

    1. Djebbari, Habiba & Smith, Jeffrey, 2008. "Heterogeneous impacts in PROGRESA," Journal of Econometrics, Elsevier, vol. 145(1-2), pages 64-80, July.

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    Bayesian econometrics; Treatment effects;


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